In this project, our goal is to design reliable machine learning methods that can be understood by humans. We propose to tackle this goal in three different ways: (1) Interpreting the decision of black-box AI models to make it transparent, (2) making self-explainable AI models for better reliability, and (3) aligning the explanation of AI models to human annotation. Based on our prior works that studied the robustness of black-box model to geometric transformation (AAAI’20) and self-explainable model by simple modification of Class Activation Map but with better explainability (ICCV’21), the first step is to propose a reliable explanation by estimating uncertainty over the explanation. We plan this as a collaborative project between the EML lab (lead by Prof. Akata) at the University of Tübingen who studies multimodal explanations and the Willow lab (co-lead by Prof. Schmid) who studies visual recognition.